Terminology

This page contains information about terminology/abbreviations/acronyms etc.

Abbreviations

OL - Opening lead or opening leader depending on context
SK - Self kibitzer (or self kibitzing)
VACB - Virtual ACBL club

Acronyms

DDOLAR: Double dummy opening lead accuracy rate (%)

For each lead, look at the double dummy analysis. The lead is either "Good" (does not give up a trick against double dummy) or "Bad" (gives up a trick). For each pair/player, divide the number of good leads by the total number of leads for the DDOLAR.

The term is abbreviated as DDOLAR, pronounced "D-Dollar".

Top experts in IMP play average just under 81%.

ACBL club players in MP play average just under 80%.

ACBL virtual club (VACB) players in MP play average just under 79%.

Any value above 85% is generally suspicious; however you must also look at the number of boards being analyzed. Someone who has a DDOLAR of 90% with 20 boards is far less suspicious than a player with a DDOLAR of 85% over 300 boards.

Examples: In face-to-face play (FTF) Eric Rodwell DDOLAR is 82.07%, Jeff Meckstroth is 80.17%.

Top experts average 81%. ACBL BBO players average 80%. ACBL Virtual Club players average 79%. The range for players with the most data (6,000 total boards played) in ACBL BBO events is 74.60% to 87.45%.

ADDOLAR: Actual double dummy opening lead accuracy rate (%)

For each lead, look at the double dummy analysis. If the lead is double dummy irrelevant (i.e. it does not matter what you lead according to double dummy), throw the lead out.

The lead is then either "Good" (does not give up a trick against double dummy) or "Bad" (gives up a trick).

The term is abbreviated as ADDOLAR, pronounced "A-D-Dollar".

Top experts in IMP play average just under 74.7%.

ACBL club players in MP play average about 73.7%.

ACBL virtual club players in MP play average just under 72.7%.

Any value above 80% is suspicious; however you must also look at the number of boards being analyzed. Someone who has a ADDOLAR of 85% with 20 boards is far less suspicious than a player with a ADDOLAR of 80% over 300 boards.

Examples: In face-to-face play (FTF) Eric Rodwell ADDOLAR is 76.42%, Jeff Meckstroth is 73.06%.

DCER: Declarer error rate (%)

For every card played by declarer, including cards called from dummy, compare the card played against double dummy analysis. The card is either "good" or "bad" (gives up one or more tricks).

The declarer error rate (DCER) is the percentage of "bad" cards against the total number of cards played.

The DCER is be affected by the speed of a player's claim. A player can manipulate their DCER by playing out the hand instead of claiming when they should. Therefore this is not a good metric to use.

DECWER: Declarer weighted error rate (%)

The declarer weighted error rate (DECWER) ignores the issue of the speed of the claim.

DECWER is similar to the DCER except it assumes that there are 24 cards played by declarer in every hand. The play to the thirteenth round is fixed. Declarer calls cards from both dummy and her own hand.

For every card played by declarer, including cards called from dummy, compare the card played against double dummy analysis. The card is either "good" or "bad" (gives up one or more tricks).

The weighted error rate is the percentage of "bad" cards against the (number of boards played * 24).

World class DECWER is under 1.80%, for example, Eric Rodwell is 1.73%, Jeff Meckstroth is 1.78%. Almost no top players are under 1.5% for a large number of boards. Average for top world class players (1,000 total boards played) is 1.89% with a range of 1.56% to 2.45%, median is 1.87%.

ACBL BBO players average 3.15%. Below 2.5% is flight A. The range for players with the most data in ACBL BBO events is 1.61% to 4.55%.

DECWAR: Declarer weighted accuracy rate

Opposite of DECWER. I usually multiply this by 1,000 to give a 5 digit number. World class DECWAR is over 98,200, for example, Eric Rodwell is 98,270 (1.73%), Jeff Meckstroth is 98,220 (1.78%). I am dropping the use of DECWAR, but it is still mentioned in older pages of my web site.

DFER: Defensive error rate (%)

The defensive error rate (DFER) uses the same calculations as the DCER except it is on defensive play and it ignores the opening lead. I am dropping the use of DFER, but it is still mentioned in older pages of my web site.

DEFWER: Defensive weighted error rate (%)

The defensive weighted error rate (DEFWER) uses the same calculations as the DECWER except it is on defensive play and it ignores the opening lead.

The weighted error rate is the percentage of "bad" cards (ignoring the opening lead) against the total number of possible cards played per board. For the opening leader, there are 11 possible cards, for the partner of the opening leader, there are 12.

World class DEFWER is under 1.20%, for example, Eric Rodwell is 1.05%, Jeff Meckstroth is 1.19%. Average for top world class players (1,000 total boards played) is 1.36% with a median of 1.36%. It is difficult to know the true range because the 176 players in this list include several collusive cheaters. The lowest ranked player (not a sponsor) is 1.78%.

ACBL BBO players average 2.54%. The range for players with the most data in ACBL BBO events is 0.84% to 3.53%. Yes - ACBL has some players that defend better than the world's best with 6,000+ total boards played.

DEFWAR: Defensive weighted accuracy rate

Opposite of DEFWER. I usually multiply this by 1,000 to give a 5 digit number. I am dropping the use of DEFWAR, but it is still mentioned in older pages of my web site.

ODECWER: Opponents DECWER

The opponents DECWER when you defend.

ODEFWER: Opponents DEFWER

The opponents DEFWER when you declare.

ODDOLAR: Opponents DDOLAR

The opponents DDOLAR when you declare.

OADDOLAR: Opponents ADDOLAR

The opponents ADDOLAR when you declare.

One Name Study

A one name study involves finding all boards played by a player over a specific time frame. This would include practice matches, social games etc. The intent is to make sure that no boards are excluded from a case. The relevant boards can then be selected, for example, practice matches should not be included for a cheating case.

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